{"title":"多分辨率图像压缩的双正交小波","authors":"Shubhangi Krishna, Dharmendra Kumar, V. Dwivedi","doi":"10.1109/PARC52418.2022.9726558","DOIUrl":null,"url":null,"abstract":"In this paper, biorthogonal wavelet transform is used for compression of high resolution aerial images. Biorthogonal wavelets enlarge the family of orthogonal wavelets and can be subjected to inversion. The Biorthogonal wavelet family uses distinct wavelet and scaling functions for the decomposition and reconstruction of images and signals. Biorthogonal wavelets also demonstrates the feature of linear phase by using two wavelets for decomposition and reconstruction. This basically means that images are decomposed by one family and reconstructed by another.A methodology is designed using bi-orthogonal wavelets, preserving 8% of the wavelet coefficients after image compression. Even though there is a significant reduction in both the dimensions and size of the selected images, there is almost negligible damage to the visual properties of the images. The originality of the selected images remains unhindered throughout the course of action which conveys how efficient biorthogonal wavelets are in compression of high resolution images.","PeriodicalId":158896,"journal":{"name":"2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Biorthogonal Wavelets for Multiresolution Image Compression\",\"authors\":\"Shubhangi Krishna, Dharmendra Kumar, V. Dwivedi\",\"doi\":\"10.1109/PARC52418.2022.9726558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, biorthogonal wavelet transform is used for compression of high resolution aerial images. Biorthogonal wavelets enlarge the family of orthogonal wavelets and can be subjected to inversion. The Biorthogonal wavelet family uses distinct wavelet and scaling functions for the decomposition and reconstruction of images and signals. Biorthogonal wavelets also demonstrates the feature of linear phase by using two wavelets for decomposition and reconstruction. This basically means that images are decomposed by one family and reconstructed by another.A methodology is designed using bi-orthogonal wavelets, preserving 8% of the wavelet coefficients after image compression. Even though there is a significant reduction in both the dimensions and size of the selected images, there is almost negligible damage to the visual properties of the images. The originality of the selected images remains unhindered throughout the course of action which conveys how efficient biorthogonal wavelets are in compression of high resolution images.\",\"PeriodicalId\":158896,\"journal\":{\"name\":\"2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC)\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PARC52418.2022.9726558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PARC52418.2022.9726558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Biorthogonal Wavelets for Multiresolution Image Compression
In this paper, biorthogonal wavelet transform is used for compression of high resolution aerial images. Biorthogonal wavelets enlarge the family of orthogonal wavelets and can be subjected to inversion. The Biorthogonal wavelet family uses distinct wavelet and scaling functions for the decomposition and reconstruction of images and signals. Biorthogonal wavelets also demonstrates the feature of linear phase by using two wavelets for decomposition and reconstruction. This basically means that images are decomposed by one family and reconstructed by another.A methodology is designed using bi-orthogonal wavelets, preserving 8% of the wavelet coefficients after image compression. Even though there is a significant reduction in both the dimensions and size of the selected images, there is almost negligible damage to the visual properties of the images. The originality of the selected images remains unhindered throughout the course of action which conveys how efficient biorthogonal wavelets are in compression of high resolution images.